Laurie A. Blanchard

Validation of a Temperature Telemetry System during Moderate and Strenuous Exercise

1. 1.Esophageal temperature [Tes (18.0 ± 6.1 min)] and pill temperature [Thti (25.3 ± 9.1 min)] reached steady state faster (P < 0.05) during moderate exercise (40 min at 40% peak VO2) than rectal temperature [Tre (37.3 ± 4.6 min)] at Ta = 29°C, Tdp = 11°C. Steady-state exercise temperatures were lower for Tes = 37.18 ± 0.18°C andThti = 37.20 ± 0.32°C than Tre = 37.46 ± 0.15°C (P < 0.05). 2. 2.During moderate exercise the change in core temperature per time (slope) was greater (P < 0.05) for Tes (0.050 ± 0.013°C min−1) than Thti (0.031 ± 0.014°C min−1) and Tre (0.018 ± 0.005°C min−1. During intense exercise the change in Tes per minute was twice that for Thti and 5 times that for Tre. 3. 3.Overall, Thti tracked dynamic changes in core temperature significantly faster than Tre, although Thti did not track dynamic changes as well or as consistently as Tes. The concept of using a temperature sensor in a pill may be useful clinically, but mobility of the pill makes this temperature measurement less suitable for research than esophageal or rectal temperature measurements.

Expanded prediction equations of human sweat loss and water needs

The Institute of Medicine expressed a need for improved sweating rate (msw) prediction models that calculate hourly and daily water needs based on metabolic rate, clothing, and environment. More than 25 years ago, the original Shapiro prediction equation (OSE) was formulated as msw (g.m(-2).h(-1))=27.9.Ereq.(Emax)(-0.455), where Ereq is required evaporative heat loss and Emax is maximum evaporative power of the environment; OSE was developed for a limited set of environments, exposures times, and clothing systems. Recent evidence shows that OSE often overpredicts fluid needs. Our study developed a corrected OSE and a new msw prediction equation by using independent data sets from a wide range of environmental conditions, metabolic rates (rest to <or=450 W/m2), and variable exercise durations. Whole body sweat losses were carefully measured in 101 volunteers (80 males and 21 females; >500 observations) by using a variety of metabolic rates over a range of environmental conditions (ambient temperature, 15-46 degrees C; water vapor pressure, 0.27-4.45 kPa; wind speed, 0.4-2.5 m/s), clothing, and equipment combinations and durations (2-8 h). Data are expressed as grams per square meter per hour and were analyzed using fuzzy piecewise regression. OSE overpredicted sweating rates (P<0.003) compared with observed msw. Both the correction equation (OSEC), msw=147.exp (0.0012.OSE), and a new piecewise (PW) equation, msw=147+1.527.Ereq-0.87.Emax were derived, compared with OSE, and then cross-validated against independent data (21 males and 9 females; >200 observations). OSEC and PW were more accurate predictors of sweating rate (58 and 65% more accurate, P<0.01) and produced minimal error (standard error estimate<100 g.m(-2).h(-1)) for conditions both within and outside the original OSE domain of validity. The new equations provide for more accurate sweat predictions over a broader range of conditions with applications to public health, military, occupational, and sports medicine settings.

Methods of Evaluating Protective Clothing Relative to Heat and Cold Stress: Thermal Manikin, Biomedical Modeling, and Human Testing

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.

Mathematical prediction of core body temperature from environment, activity, and clothing: The heat strain decision aid (HSDA)

Physiological models provide useful summaries of complex interrelated regulatory functions. These can often be reduced to simple input requirements and simple predictions for pragmatic applications. This paper demonstrates this modeling efficiency by tracing the development of one such simple model, the Heat Strain Decision Aid (HSDA), originally developed to address Army needs. The HSDA, which derives from the Givoni-Goldman equilibrium body core temperature prediction model, uses 16 inputs from four elements: individual characteristics, physical activity, clothing biophysics, and environmental conditions. These inputs are used to mathematically predict core temperature (Tc) rise over time and can estimate water turnover from sweat loss. Based on a history of military applications such as derivation of training and mission planning tools, we conclude that the HSDA model is a robust integration of physiological rules that can guide a variety of useful predictions. The HSDA model is limited to generalized predictions of thermal strain and does not provide individualized predictions that could be obtained from physiological sensor data-driven predictive models. This fully transparent physiological model should be improved and extended with new findings and new challenging scenarios.

Planning Military Drinking Water Needs: Development of a User-Friendly Smart Device Application

Potable water is essential to maintain health and sustain military operations, but carrying and transporting water is a major logistical burden. Planning for group drinking water needs is complex, requiring understanding of sweat losses on the basis of intensity of activity, clothing biophysical parameters, and environmental conditions. Use of existing prediction equations is limited to tabled doctrine (e.g., Technical Bulletin, Medical 507) or to individuals with extensive expertise in thermal biophysics. In the present project, we translated the latest updated equations into a user-friendly Android application (Soldier Water Estimation Tool, SWET) that provides estimated drinking water required from 5 simple inputs based upon a detailed multiparametric sensitivity analysis. Users select from multiple choice inputs for activity level, clothing, and cloud cover, and manually enter exact values for temperature and relative humidity. Total drinking water needs for a unit are estimated in the Mission Planner tool on the basis of mission duration and number of personnel. In preliminary user acceptability testing, responses were overall positive in terms of ease of use and military relevance. Use of SWET for water planning will minimize excessive load (water) carriage in training and mission settings, and will reduce the potential for dehydration and/or hyponatremia to impair Warfighter health and performance.

Cardiorespiratory responses to heavy military load carriage over complex terrain

This study examined complex terrain march performance and cardiorespiratory responses when carrying different Soldier loads. Nine active duty military personnel (age, 21 ± 3 yr; height, 1.72 ± 0.07 m; body mass (BM), 83.4 ± 12.9 kg) attended two test visits during which they completed consecutive laps around a 2.5-km mixed terrain course with either a fighting load (30% BM) or an approach load (45% BM). Respiratory rate and heart rate data were collected using physiological status monitors. Training impulse (TRIMP) scores were calculated using Banisters formula to provide an integrated measure of both time and cardiorespiratory demands. Completion times were not significantly different between the fighting and approach loads for either Lap 1 (p = 0.38) or Lap 2 (p = 0.09). Respiration rate was not significantly higher with the approach load than the fighting load during Lap 1 (p = 0.17) but was significantly higher for Lap 2 (p = 0.04). However, heart rate was significantly higher with the approach load versus the fighting load during both Lap 1 (p = 0.03) and Lap 2 (p = 0.04). Furthermore, TRIMP was significantly greater with the approach load versus the fighting load during both Lap 1 (p = 0.02) and Lap 2 (p = 0.02). Trained military personnel can maintain similar pacing while carrying either fighting or approach loads during short mixed terrain marches. However, cardiorespiratory demands are greatly elevated with the approach load and will likely continue to rise during longer distance marches.